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Record W2806719041 · doi:10.1177/2514848618777162

The antinomies of nature and space

2018· article· en· W2806719041 on OpenAlexaff
Rosemary‐Claire Collard, Leila M. Harris, Nik Heynen, Lyla Mehta

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsSpace (punctuation)PhilosophyLinguistics

Abstract

fetched live from OpenAlex

Maria, Irma, Harvey, Katrina – these have become more than names. They represent several of the most recent hurricanes that have devastated communities across North America and the Caribbean. As nature–society encounters, these massive storms elevated a range of historical, sociocultural, and political economic issues to the fore – from colonialism and race, to growing patterns of inequality, government mismanagement, and the politics of knowledge related to climate change. Media coverage of these events recalls early scholarly interventions by critical disaster studies scholars that highlighted the myriad ways that ‘disasters’ are not only results of climatic or geologic forces, but are connected to historical, sociocultural, and institutional dynamics. It has become increasingly accepted that race, caste, ethnicity, income, and other patterns of inequality must be considered when evaluating the risk and outcomes of storms, earthquakes, droughts or other ‘natural’ events. Indeed, the recent aftermath of hurricanes in the Caribbean cast a spotlight on long-standing political and economic inequalities between the U.S. and its quasi-imperial territories of Puerto Rico and the Virgin Islands – whether about the pathways and futures of inequality and vulnerability on the islands, or the slow and inadequate governmental response.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.275
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations47
Published2018
Admission routes1
Has abstractyes

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